Data Visualization with Matplotlib

Reviewed & published by Brayan K

Matplotlib is Python's most established plotting library. With a handful of pyplot calls you can turn raw numbers into line charts, scatter plots, bar charts, and histograms — and the object-oriented API gives you full control over every figure and axes.

Part of the free Python course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

What You'll Learn in This Lesson

• • The pyplot interface: plot, scatter, bar, hist
• • Adding labels, titles, and legends
• • Figures, axes, and multi-panel subplots
• • Styling colors, markers, and line styles
• • Saving figures with savefig
• • The object-oriented fig, ax = plt.subplots() API vs the pyplot state machine

📈 1. Your First Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [1, 4, 9, 16]

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y = x squared")
plt.title("My First Plot")
plt.show()

⚠️ Requires pip install matplotlib and a local Python display

🔵 2. Scatter, Bar, and Histogram

Different data calls for different chart types:

import matplotlib.pyplot as plt

# Scatter: relationship between two variables
plt.scatter([1, 2, 3], [3, 1, 2])
plt.show()

# Bar: compare categories
plt.bar(["A", "B", "C"], [5, 7, 3])
plt.show()

# Histogram: distribution of one dataset
plt.hist([1, 1, 2, 3, 3, 3, 4], bins=4)
plt.show()

🏷️ 3. Labels, Titles, and Legends

Annotate plots with the label= argument plus legend():

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 2, 3], label="growth")
plt.plot([1, 2, 3], [3, 2, 1], label="decline")
plt.title("Two Trends")
plt.xlabel("time")
plt.ylabel("value")
plt.legend()   # shows the labels
plt.show()

🧩 4. The Object-Oriented API

Create explicit Figure and Axes objects with plt.subplots():

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [2, 4, 6])
ax.set_title("OO API")
ax.set_xlabel("x")
ax.set_ylabel("y")
plt.show()

This is the recommended style for anything beyond a quick one-off chart.

🟢 Worked Example: a chart you can check without seeing it

# 🟢 WORKED EXAMPLE — build a two-line chart, save it, and prove what it contains
import matplotlib
matplotlib.use("Agg")          # "Agg" = draw into a file, never try to open a window
import matplotlib.pyplot as plt
from pathlib import Path

months  = ["Jan", "Feb", "Mar", "Apr", "May"]
signups = [120, 150, 145, 190, 240]
cancels = [10, 12, 30, 18, 22]

# One figure, one axes. figsize is in inches: 8 wide by 4.5 tall.
fig, ax = plt.subplots(figsize=(8, 4.5))

# Each ax.plot() call adds one line to the SAME axes. label= is what the legend reads.
ax.plot(months, signups, marker="o", label="sign-ups")
ax.plot(months, cancels, marker="s", linestyle="--", label="cancellations")

ax.set_title("Sign-ups vs cancellations")   # OO API: set_title, not plt.title
ax.set_xlabel("Month")
ax.set_ylabel("People")
ax.legend()                    # without this call the label= values are never shown
ax.grid(True, alpha=0.3)       # alpha=0.3 keeps the grid faint behind the data

fig.tight_layout()             # stops the labels being clipped at the edges
fig.savefig("signups.png", dpi=120)     # the extension picks the format: .png, .pdf, .svg

# ---- proof it worked, in text, because this editor only shows printed output ----
print("lines drawn   :", len(ax.lines))
print("legend labels :", [t.get_text() for t in ax.get_legend().get_texts()])
print("title         :", ax.get_title())
print("axis labels   :", ax.get_xlabel(), "/", ax.get_ylabel())
print("x tick labels :", [t.get_text() for t in ax.get_xticklabels()])
print("net growth    :", [s - c for s, c in zip(signups, cancels)])
print("file written  :", Path("signups.png").exists())

# ✅ Expected output:
# lines drawn   : 2
# legend labels : ['sign-ups', 'cancellations']
# title         : Sign-ups vs cancellations
# axis labels   : Month / People
# x tick labels : ['Jan', 'Feb', 'Mar', 'Apr', 'May']
# net growth    : [110, 138, 115, 172, 218]
# file written  : True

Everything you drew is still queryable afterwards — ax.lines, ax.get_title(), the legend's texts. A chart is not a picture until you save it; until then it is a Python object you can inspect, tweak and test. That is exactly how you would write an automated test for a plotting function.

🎯 Your Turn: chart a poll result

Three blanks: pick the right chart type for categories, set a title, and label the axis. Use exactly the strings shown in the hints so your output matches the expected output.

# 🎯 YOUR TURN — fill in the blanks marked with ___
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

languages = ["Python", "JS", "Go", "Rust"]
votes     = [42, 35, 12, 18]

fig, ax = plt.subplots(figsize=(6, 4))

# 1) You are comparing four separate categories, not a trend over time.
ax.___(languages, votes, color="steelblue")   # 👉 plot, scatter, bar or hist?

# 2) Give it a title. On an Axes the method is set_something.
ax.___("Favourite language poll")             # 👉 which Axes method sets the title?

# 3) Say what the height of a bar means.
ax.set_ylabel(___)                            # 👉 the text "Votes", quotes included

fig.tight_layout()
fig.savefig("poll.png", dpi=120)

# ---- printed check ----
print("container type:", type(ax.containers[0]).__name__)
print("bars drawn    :", len(ax.containers[0]))
print("title         :", ax.get_title())
print("y label       :", ax.get_ylabel())
print("tallest bar   :", languages[votes.index(max(votes))], max(votes))

# ✅ Expected output:
# container type: BarContainer
# bars drawn    : 4
# title         : Favourite language poll
# y label       : Votes
# tallest bar   : Python 42

If blank 1 goes wrong you will see it immediately: ax.containers is empty for a line chart, so the first print raises IndexError: list index out of range instead of printing BarContainer.

🪟 5. Multiple Subplots

Pass nrows and ncols to get a grid of axes:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=1, ncols=2)
axes[0].plot([1, 2, 3], [1, 2, 3])
axes[0].set_title("Left")
axes[1].bar(["A", "B"], [3, 5])
axes[1].set_title("Right")
fig.tight_layout()
plt.show()

axes is an array you index into; each entry is its own Axes you draw on.

🎨 6. Styling

Control color, marker, and line style:

import matplotlib.pyplot as plt

plt.plot(
    [1, 2, 3], [1, 4, 9],
    color="green",
    linestyle="--",
    marker="o",
    linewidth=2,
)
plt.grid(True)
plt.show()

💾 7. Saving Figures

Write the figure to a file with savefig:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 2, 3])
plt.title("Saved Chart")

# Format is chosen from the file extension
plt.savefig("chart.png", dpi=150, bbox_inches="tight")
# also works: chart.pdf, chart.svg

Call savefig before show() — showing can clear the current figure in some backends.

🏁 Mini-Challenge: a two-panel weather figure

No blanks — a brief and an empty outline. Build one figure with two panels side by side: a line chart of temperature on the left, a bar chart of rainfall on the right, each with its own title and y-axis label. Save it as week.png.

Use the exact titles "Temperature" and "Rainfall" so the printed check matches the expected output below.

# 🏁 MINI-CHALLENGE — write it yourself
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path

days        = ["Mon", "Tue", "Wed", "Thu", "Fri"]
temperature = [12, 14, 13, 17, 19]
rainfall    = [4, 0, 9, 2, 0]

# 1. fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
#    axes is an ARRAY: axes[0] is the left panel, axes[1] is the right one.
# 2. Left panel: a line of temperature against days, marker="o",
#    title "Temperature", y label "degrees C".
# 3. Right panel: bars of rainfall against days,
#    title "Rainfall", y label "mm".
# 4. fig.tight_layout(), then save the figure as week.png at dpi=120.

# your code here


# ---- printed check: leave this at the bottom ----
print("panels       :", len(fig.axes))
print("left title   :", fig.axes[0].get_title())
print("right title  :", fig.axes[1].get_title())
print("left lines   :", len(fig.axes[0].lines))
print("right bars   :", len(fig.axes[1].containers[0]))
print("warmest day  :", days[temperature.index(max(temperature))])
print("file written :", Path("week.png").exists())

# ✅ Expected output:
# panels       : 2
# left title   : Temperature
# right title  : Rainfall
# left lines   : 1
# right bars   : 5
# warmest day  : Fri
# file written : True

🎉 Conclusion

With Matplotlib you can:

✔ Draw line, scatter, bar, and histogram charts

✔ Annotate with labels, titles, and legends

✔ Build multi-panel figures with subplots

✔ Style colors, markers, and grids

✔ Save publication-ready images with savefig

Reach for the fig, ax API whenever a plot grows beyond a quick sketch.

📋 Quick Reference — Matplotlib

🎉 Great work! You've completed this lesson.

You can now turn data into clear, styled charts and export them for reports.

Practice quiz

Continue this course

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